Abstract. In this paper, we consider a low-rank tensor recovery problem. Based on the tensor singular value decomposition (t-SVD), we propose the ratio of the tensor nuclear norm and the tensor Frobenius norm (TNF) as a novel nonconvex surrogate of tensor’s tubal rank. The rationale of the proposed model for enforcing a low-rank structure is analyzed as its theoretical properties. Specifically, we introduce a null space property (NSP) type condition, under which a low-rank tensor is a local minimum for the proposed TNF recovery model. Numerically, we consider a low-rank tensor completion problem as a specific application of tensor recovery and employ the alternating direction method of multipliers (ADMM) to secure a model solution with guaranteed subsequential convergence under mild conditions. Extensive experiments demonstrate the superiority of our proposed model over state-of-the-art methods.

Large area array CCD splicing technology and hardware damage lead to the lack of information in high-resolution remote sensing images (HRSIs), which seriously restricts the application of remote sensing images. However, the complex spatial geographical relationship and the serious spatial heterogeneity will cause texture blur and incomplete structure, especially in the boundary and highly textured regions of HRSI. To deal with the challenge, this article proposes a coarse-to-fine deep generative model with a novel spatial semantic attention (SSA) mechanism. SSA is deployed in the encoder and decoder of the network to ensure the continuity of local features and the relevance of global semantic information and embedded in fine network. In the coarse network, a multilevel loss is proposed to provide more accurate information. Fine network performs more detailed image inpainting operations based on coarse network. Extensive experiments on three datasets, including GF2 satellite images, aerial images, and natural ones, demonstrate that our proposed model has better inpainting effect than the state-of-the-art algorithms.

CBERS02B with three payloads onboard was successfully launched on September 19, 2007 in order to ensure the continuity of CBERS series and CCD is one of three payloads. Calibration of CCD is a precursor for its quantitative application because there isn't onboard calibrator for CCD. A comprehensive vicarious calibration and validation campaign of CCD was performed at Gongger test site on October 12, 2007. The reflectance-based calibration method was used in this campaign with the ground measurements of the surface reflectance and atmospheric characteristics. Then 6S, a radiative transfer code, was used to compute the top-of-atmosphere(TOA) radiance at the sensor. Calibration result was obtained for CCD showing that some change brought to the CCD after launch, especially band 1 and band 2. The in-situ field measurement at the Dunhuang test site was collected validating that the calibration result was good expect for band 4.

This paper, we detect clouds in China regions from combination of POLarization and Directionality of the Earth's Reflectances(POLDER) data and Moderate Resolution Imaging Spectroradiometer(MODIS) data, based on the spectral, multi- angular, and polarized characteristics of cloud. Four tests are applied to the measurements. The first one is blue channel reflectance test. The second one is the test on polarization at 865 nm. The third one is the test on reflectance at 1380 nm. The fourth one is the test on reflectance at 645 nm and 1640 nm. At last, the performance of method is evaluated using a large dataset of surface face observations of cloud cover. The result demonstrate the methods of cloud detection are feasible and believable..

On 29 and 31 May 2006, a comprehensive vicarious calibration experiment for the Moderate Resolute Imaging Spectroradiometer(MODIS) visible and near-infrared bands was performed at Gongger test site located in Inner Mongolia, which is a flat and uniform area. The reflectance-based method was used for calibration of MODIS visible and near-infrared bands. In situ measurements of surface and atmospheric conditions were carried out. By computing the surface reflectance of the site, it was concluded that the site was appropriate for calibration because of its stable and uniform characteristics. These data were then inputted to a radiative transfer code, 6S, to compute top-of- atmosphere (TOA) radiances and TOA reflectances, which were compared with the MODIS on-board calibration results. The in situ estimated results were in good agreement with the MODIS on-board calibration results on May 31 with the variations about 2%, while vicarious calibration results on May 29 were slightly inconsistent with those of on-board calibration, whose differences were about 7%.

Measurement of polarized properties of the skylight from the ground is one of an effective means of investigating the optical and physical parameters. A new system to measure the natural skylight polarized radiance distribution has been developed. The system is based on the field spectrometer Analytical Spectral Devices (ASD) with a dichroic linear polarizing filter. With this system sequences of radiance data were obtained which can be determined the linear polarization components of the skylight. The ground-based measurements are compared with simulations based on semi-empirical Rayleigh model.

Numerous researchers have observed that the distribution of brightness temperature field over an agricultural canopy strongly depended on canopy biophysical and phenological features coupling with environment conditions. Recently, an in situ experiment was conducted over a maize canopy in Avignon of France to investigate the temporal variation of brightness temperature distributions as a function of canopy geometrical structure. The experiment lasted 3 months throughout maize growth cycle. The results revealed that component brightness temperature (CBT) values are not independent with each other. The correlation of sunlit soil temperature minus vegetation temperature (Tsb-Tv) and shaded soil temperature minus vegetation temperature (Tso-Tv) is 0.85 with a RMSE of 1.1degC. In the analysis of maize geometrical structure when it was within its first two of three growth stages, good relations have been found between geometrical parameters with leaf area index LAI. To analyze the relation between CBT and LAI, the differences of CBTs were compared with LAI as a function of date of year, where we found that Tsb-Tsohas the best relation with LAI. An initial interpretation was given to this result, which is still under further analysis based on more experiment observations. The tie between CBT and LAI is expected to be utilized in the researches on canopy energy balance estimation directly

A field experiment has been performed from May to August in 1999 over a maize canopy at INRA Avignon branch in south France. Directional brightness temperature (DBT) features were extracted from thermal infrared (TIR) images captured by a TIR camera mounted upon a crane platform. Different from the method with a goniometer, another approach based on the different principles has been developed that the camera can view different part of the field to collect DBT of different angles by means of rotating to different directions instead of moving the camera to make it always focus on a defined sample area. After the description of the measurement method, the evaluation of data acquisition is presented, where the quality and limits of this measurement method are discussed. In this study, a compromise is made in sampling design based on the analysis of canopy heterogeneity and FOV effect. An example of acquiring hemispherical DBT under natural conditions was given at last. In the analysis of the measurement at 13:10 of local time on June 24, 1999, the results showed the spatial heterogeneity of field thermal radiance played as the most important role. Using the new sampling method, the total error for acquiring hemispherical DBT was about 1.4degC in maximum with 95% confidence, when the amplitude of directional variation was larger than 7degC. The errors changed with measurement conditions, which made it different for each measurement

A novel method was proposed to simulate heat fluxes above partially vegetated surfaces. The interaction of the heat fluxes between the two components (soil and foliage) in canopy was modeled using the concept of "micro-advection", which refers to small-scale movement of air in a restricted area. To adjust the heat balance of the local plant-atmosphere system is the main function of the micro-advection in this model, i.e., part of the heat emanated from soil surface is transported to the foliage surface and then consumed by the transpiration effect of the leaves in this process. The magnitude of this part of heat transport between components can be estimated from the temperature gradient between them as well as the diffusion coefficients of them. Therefore, the overall effect of the micro-advection is to diminish the total sensible heat flux and increase the level of total latent heat flux. This model was validated using the data of row crop, and the result showed good agreement with field turbulent measurements.

The paper puts forward Tupu methodology of land-use/land cover change and names the basic synthetic unit of spatial-temporal information as Tupu unit. Tupu unit is the synthetic unit for geospatial-temporal analysis, which is synthesized by "spacemiddotattributemiddotprocess" features and composed of relatively homogeneous geographic unit and temporal unit. The Tupu unit is the fundamentally functional unit in integrated spatial-temporal analysis of Geoscienccs. Same as the basic geographic unit, division of relatively homogeneous Tupu unit is very important. As Tupu unit is also in series of multicategories at multiscales, classification of Tupu unit on a certain scale is heavily depending on the goal of research

As one of the ultimate approaches to clearly define and understand directional features of canopy thermal radiation, ground level experimental studies of directional brightness temperature (DBT) have been concerned for many years. In this study, a recent crane based thermal camera system developed by INRA-Avignon of France is presented, and then four measuring methods for hemispherical DBT observations are introduced. These methods are based on different measurement principles: (1) view the same surface from different angle by moving the crane and platform to assure the sensor always toward the target; (2) view different part of the field to collect DBT of different angles by means of rotating the camera to different directions instead of moving the camera; (3) view different part of the field by moving the camera along the row direction; and (4) scan the field with a high imaging frequency while the camera moves along the crane bar vertical to the row direction. During the comparison of these methods, special emphasis is on the analysis of temporal and spatial variations of maize canopy brightness temperature distribution, from which the main errors of these methods are analyzed. The study also presents common shortcomings and limitation of the field observations with this system, such as the less capacity of hot spot observation and the sensitivities to field environmental influences. Finally, recommendations for optimal field measurement of hemispherical DBT with the system are given from an application-oriented viewpoint.

A modelling study on the hemispherical variations of directional brightness temperature (DBT) for row-structured building arrays was carried out based on the ground observations over a residential in urban areas. The model assumes that the DBT is a function of component brightness temperatures and their directional fractions. Their fractions in the scene depend on sun-view geometry and the geometry of buildings. The methodology for brightness temperature component classification and temporal variations of component values were analyzed based on the experiment dedicated to urban brightness temperature distribution. Results reveal that the number of typical objects of an urban area and their brightness temperature values vary with time of day due to the complex of urban thermal feature and geometrical structures. Generally, the trend of brightness temperature variation is similar for asphalt road and concrete place, grass and trees, walls of the building toward different directions, and the windows of the walls. In the simulation of urban areas hemispherical DBT, the results reveal an evident row-direction-oriented stripe in DBT polar map, where no hot spot appears. As an initial attempt, the research only focus on the simplified conditions, more complicated structure may be considered in the further researches.

A study on modeling the variations of directional brightness temperature (DBT) for row-structure crops was carried out with the images captured by a large-aperture thermal infrared camera over a maize canopy. The model assumes that the DBT is a function of target component brightness temperatures and their directional fractions. The canopy has three brightness temperature components: the sunlit soil, the shaded soil, and the vegetation. Their fractions in the scene depend on the sun-view geometry and the distributions of gaps within and between plant rows. To describe canopy geometrical features, a series of porous hedgerows with a rectangular cross section is used. The directional variations of gap fractions are described by the Kuusk function. The model demonstrated how the features of DBT depend on the sun-view geometry, canopy geometrical structure, and component brightness temperatures. In the simulation of DBT over a middle-density canopy near the local noontime, the results revealed an evident row-direction-oriented hot stripe in DBT polar maps, where the hot spot appeared along the sun direction. The sensitivities of the model to the input parameters were tested. Further validation demonstrated a close correlation between predicted DBT and field observations.

In order to modeling directional thermal radiation and energy balance for a partially covered canopy, surface brightness temperature is usually classified into several components. This paper researches the methodology for brightness temperature component classification and temporal variations of component number and values by an in situ experiment, dedicated to analyze maize canopy brightness temperature distribution. The measurement was carried out by using a TIR camera and a visible camera mounted on an industrial crane, the experiment lasted 3 months throughout a maize growth cycle. In the analysis of the brightness temperature, a Gaussian distribution has been assumed. Results show the number of components and their brightness temperature values vary with time of day and biomass density. Three brightness temperature components of vegetation, sunlit and shaded soil could be identified at midday during the measurement period. In the daytime, temperature variability of sunlit soil is much larger than the other two components when the canopy's density is not high. When the canopy is fully covered, vegetation brightness temperature has a wider range.

A modeling study on the variations of directional brightness temperature (DBT) for row-structure crops was carried out with the help of the images captured by a large aperture thermal infrared camera over a maize canopy. The model assumes that the DBT is a function of component brightness temperatures and their directional fractions. The canopy has three brightness temperature components: sunlit soil, shaded soil and vegetation, each component has a unique temperature value. Component fractions in the scene of view depend on sun-view geometry and the distributions of gaps within and between plant rows. To describe canopy geometrical features, a system of porous hedgerows with rectangle cross-section has been used; the directional variations of gap fraction are described by Nilson function. The model demonstrates directional variations of DBT as a function of sun-viewing geometry and canopy geometrical structure as well as component brightness temperatures. In the simulation of DBT over a middle dense canopy near the noontime, the results reveal an evident row-direction-oriented hot stripe in the DBT polar map, where appeared the hot spot along the sun direction. The sensitivities of the model to the input parameters have been tested. Further validation analysis has also been conducted which demonstrates modeled DBT agreeing closely with field observations.

Composite scene of row crops induced an unavoidable error in ground measurements of directional brightness temperature (DBT) due to the use of wide field of view (FOV). The measurement results vary with sample size and position, detector height and view direction, and bias due to project principle. This is called FOV effect. The study focuses on the estimation of FOV effect on the measurements of maize canopy using a computational geometric 2D model. The model was developed to simulate the fractional variations of canopy brightness temperature components. The simulation results revealed that the errors caused by FOV effect have a complex feature. Generally, vegetation fraction is always over counted in the nadir view, errors increase dramatically with the decrease of detector height as well as the enlargement of sample size, the deviation of the error corresponding to detect position is small; in oblique view, the errors are limited to a low level due to an effect called compensation effect. However, the deviation of the error keeps large when the sample size is small. Nevertheless, the best approach to reduce FOV effect in ground observation is levering the detector to a higher altitude as the model suggested.

In order to estimate directional variation of gap fraction over a high dense maize canopy, a geometric optical and radiative transfer (GORT) model was improved to simulate the hemispherical gap fraction, the model was validated by a crane borne experiment using a narrow FOV thermal infrared camera conducted at night. The research revealed that the path length is a function of canopy geometrical structure, view direction and position, which leads to the row effect on hemispherical gap fraction: azimuthal variation of gap fraction is insignificant except for the observations parallel to the rows. For dense canopies, the value of gap fraction declined quickly in small view zenith rather than in large view zenith range, which leads the curves to show a concave shape. The experiment was conducted at 22 h local time on July 26 in 1999 (LAI=5) for the validation. At the time, brightness temperatures of leaves and soil had Gaussian distribution, their mean values presented a significant difference (24.3/spl deg/C and 26.5/spl deg/C) comparing to their small standard deviations (0.52 degC and 0.44 degC), gap fraction could be discriminated from canopy background. Observations showed that most gaps appeared between the adjacent rows, which lead the high dense canopy still to keep row feature in thermal infrared images. As conclusion of the comparison, the model could capture the main features of the measured gap fraction. With a proper adjustment of input leaf optical parameters, the simulated gap fraction showed a fairly good agreement with observed gap fraction.

A field experiment has been conducted from May to August in 1999 over a maize canopy at INRA Avignon branch in France. The experiment covered the whole growth period of maize plants in order to observe directional brightness temperature (TBD) variations of row structure canopy as a function of measuring time and date. The TBD was extracted from thermal infrared (TIR) images captured by a TIR camera mounted upon a crane. The results show that TBD were highly date and time specific. Comparing the variations of DBT near the noontime with different biomass, the measurements over a middle dense canopy revealed an evident row-direction-oriented hot stripe in the DBT polar map, where a hot spot appeared along the solar direction. Similarly, for a low cover condition, a wider hot stripe appeared in the polar map, the hottest area in the band is around the solar position. However, for a high biomass condition when the field was nearly wholly covered by the leaves, the lowest temperature appeared along the row direction, which formed a cool strip in the polar map. Comparing the variations of DBT on the same day, for a measurement over middle dense canopy, in the morning and afternoon, with a large solar zenith, the range of DBT variation decreased, hot strip features weakened and turned wider, solar position was out of the hot strip. As a conclusion of the field experiment, the maize TBD feature is dominated by sun-sensor geometry, complex effects of canopy structures coupled with spatial distributions of canopy brightness temperatures. A row structure effect appeared clearly in the TBD polar map throughout the whole measurement. Quantitative explanations are expected in further research on the physical models.

Data experiment of Mont-Carlo directional radiation transfer model for continuous vegetation was made to decide the optimum view angle of thermal temperature for reliable estimation of sensible heat flux. The true heat fluxes were simulated with classical two-layer model. The conclusions of this study are: 1) the optimum view angle varies within a large range according to the change of leaf area index, leaf angle distribution, soil moisture and other parameters, so it's difficult to define a universal optimum angle; 2) however, the fractional coverage of vegetation in FOV (field of view) under optimum angle is relatively stable and could be used in a new corrective method.

In this paper, temperature/vegetation index relation is used to evaluate soil moisture conditions by 8 km, 10-day composite AVHRR data set. To define the T/NDVI slope, window size and automatic linear fit are studied. Then T/NDVI slope is analyzed spatially and temporally. Results show that soil moisture is closely correlated to the T/NDVI slope. A new approach is proposed to estimate soil moisture availability by T/NDVI slope and T/NDVI space. Finally, soil moisture and drought distribution maps are produced in China.

In order to retrieve land surface component temperature from multi-spectral remote sensing images, such as ASTER, we choose a component equivalent emissivity model, and iterative linear regression inversion algorithm. The method is tested with ASTER image of VNIR and TIR channels, as well as supplementary and validation data acquired from ground experiment. The atmospheric effect is corrected with the dark-object method; surface structural information is derived from ASTER VNIR observations; component emissivity is measured in situ with the BOMEN MR-154 spectrometer; validation data are also measured in ground experiments. Finally, the accuracy of the results and sources of error are analyzed.

The classical Shuttleworth-Wallace two-layer model has a modification version proposed by Norman et al. (1995) and Kustas et al. (1999) to estimate evapotranspiration over row crop surfaces. However, the assumption of this modified model is controvertible, and more theoretical analysis and data validation seem necessary to figure out whether this model is a "layer" model or a "patch" model essentially. A field data validation of this kind was carried out in this paper and it was found that a "patch" method could provide more accurate results. A new method to improve S-W model for row crop was given at the end of this paper. This model uses "layer" method to account for the in-canopy exchanges and modified surface resistances to account for the heterogeneities of row crops. The data from the composite remote sensing experiment in Shunyi, Beijing April 2001 were used to validate this model. Surface fluxes were measured by Bowen ratio system while surface radiometric temperature; component temperatures and other parameters were also measured at different atmospheric boundary layer conditions. Results show that estimation of sensible and latent beat fluxes by the new model agree well with the observed fluxes.